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Transformers for Natural Language Processing

You're reading from   Transformers for Natural Language Processing Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781803247335
Length 602 pages
Edition 2nd Edition
Languages
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Author (1):
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Denis Rothman Denis Rothman
Author Profile Icon Denis Rothman
Denis Rothman
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Table of Contents (25) Chapters Close

Preface 1. What are Transformers? FREE CHAPTER 2. Getting Started with the Architecture of the Transformer Model 3. Fine-Tuning BERT Models 4. Pretraining a RoBERTa Model from Scratch 5. Downstream NLP Tasks with Transformers 6. Machine Translation with the Transformer 7. The Rise of Suprahuman Transformers with GPT-3 Engines 8. Applying Transformers to Legal and Financial Documents for AI Text Summarization 9. Matching Tokenizers and Datasets 10. Semantic Role Labeling with BERT-Based Transformers 11. Let Your Data Do the Talking: Story, Questions, and Answers 12. Detecting Customer Emotions to Make Predictions 13. Analyzing Fake News with Transformers 14. Interpreting Black Box Transformer Models 15. From NLP to Task-Agnostic Transformer Models 16. The Emergence of Transformer-Driven Copilots 17. The Consolidation of Suprahuman Transformers with OpenAI’s ChatGPT and GPT-4 18. Other Books You May Enjoy
19. Index
Appendix I — Terminology of Transformer Models 1. Appendix II — Hardware Constraints for Transformer Models 2. Appendix III — Generic Text Completion with GPT-2 3. Appendix IV — Custom Text Completion with GPT-2 4. Appendix V — Answers to the Questions

Machine Translation with the Transformer

Humans master sequence transduction, transferring a representation to another object. We can easily imagine a mental representation of a sequence. If somebody says The flowers in my garden are beautiful, we can easily visualize a garden with flowers in it. We see images of the garden, although we might never have seen that garden. We might even imagine chirping birds and the scent of flowers.

A machine must learn transduction from scratch with numerical representations. Recurrent or convolutional approaches have produced interesting results but have not reached significant BLEU translation evaluation scores. Translating requires the representation of language A transposed into language B.

The transformer model’s self-attention innovation increases the analytic ability of machine intelligence. A sequence in language A is adequately represented before attempting to translate it into language B. Self-attention brings the level of...

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